Precise geometric correction promotes usefulness of satellite images for geographical information extraction as well as for topographic correction. For Landsat TM images, we have proposed the optimization method that uses simulated direct solar irradiance to correct the scene center displacement given in the system information. However, in the case of wide area, we have found that the difference in optimized scene center displacements of different points has become large. In this paper, we propose to use regression analysis for estimating accurate scene center displacements over wide area. We apply this method to make composed Landsat images for Kitakami highlands and Aomori prefecture, which needs more than one scene with different path and row. It is shown that the estimated position errors are within one pixel for whole images.

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This paper presents an inverse analysis algorithm of liquefaction trigger-factors applying the Synthetic Aperture Radar (SAR) data observed by JERS-1. In the inverse analysis model based on the SEM (Structural Equation Modeling) approach, a Trigger Factor Influence map (termed "TFI map") could be produced through the "measurement-equation" defined by the causal factors (i.e., the geographical information (GI), the satellite SAR data) and the liquefaction trigger factors. In producing the TFI maps, the following examination cases have been executed : Case-1) Only using GI, Case-2) Using GI and the SAR data. Through hypothesis testing for evaluating model fit, the Case 2 of applying the SAR data indicates better fit of the model. Furthermore, as a practical utilization of the SAR data itself, as well as a final product, the "Risky- and Safe-side assessment" sub-area is delineated on a difference map (termed "DIF map") between the TFI maps in each case. The DIF map and its interpretation might be useful not only for estimating unobserved trigger factors as supporting and heuristic information, but also for improving the cost-effectiveness of locating the places for setting the field measuring systems.

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Global Imager (GLI) is the moderate spatial resolution imager which has 36 channels from near UV to infrared. It observed cloud and aerosol properties for global scale. One of the features of the GLI is that it has 380 nm channel in near UV region. Using this channel in RGB composite image, the aerosols spread over the land area clearly identified. The global distributions and properties of aerosols and clouds were retrieved as the standard analyses of the GLI atmospheric project. These results will be used for validating climate modes, that predict future climate on the Earth.

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The Greenhouse gases Observing Sensor on Greenhouse gases Observing SATellite (GOSAT), is a Fourier-Transform Spectrometer (FTS) to observe Greenhouse gases. GOSAT will be placed in a 666 km sun-synchronous orbit of 13 : 00 local time, with an inclination angle of 98 deg. The instrument detects the solar short wave infrared spectra (SWIR) reflected on the earth's surface as well as the thermal infrared spectra (TIR) radiated from the ground and the atmosphere. The FTS is capable of detecting three narrow bands (0.76, 1.6, and 2μm) and a wide band (5.5-14.3μm) with 0.24 cm^-1 spectral resolution.

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After the launch of Terra satellite in December of 1999, ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) has been stably collecting Earth observation data for more than 5 yeas, getting a very close cooperation with NASA. As of April 2005, total number of observed scenes is approaching to one million scenes, and covering most of the land area of the Earth multiple times. In this paper, the geometric and radiometric accuracy of ASTER data will be discussed, together with that of the DEM (Digital Elevation Model). In addition, the possibility of the application derived from its high accuracy.

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We have constructed the multi-purpose lidar system for survey of atmospheric structure over Kototabang (100.3 E, 0.2S), Indonesia in the equatorial region. In this paper, we report the multi-purpose lidar system consisting of the Mie lidar for tropospheric clouds, Raman lidar for tropospheric water vapor, the Rayleigh lidar for stratospheric and mesospheric temperature measurements, and the Fe Boltzmann lidar for temperature measurements in the mesopause region.

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